Matching Items (4)

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Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions

Description

Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex

Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO[subscript 2] emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO[subscript 2] emission product, Hestia-LA, to simulate atmospheric CO[subscript 2] concentrations across the LA megacity at spatial resolutions as fine as  ∼  1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO[subscript 2] emission products to evaluate the impact of the spatial resolution of the CO[subscript 2] emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO[subscript 2] concentrations. We find that high spatial resolution in the fossil fuel CO[subscript 2] emissions is more important than in the atmospheric model to capture CO[subscript 2] concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO[subscript 2] fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO[subscript 2] fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO[subscript 2] concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO[subscript 2] emissions monitoring in the LA megacity requires FFCO[subscript 2] emissions modelling with  ∼  1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

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Created

Date Created
  • 2016-07-22

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Reconciling the differences between a bottom-up and inverse-estimated FFCO2 emissions estimate in a large US urban area

Description

The INFLUX experiment has taken multiple approaches to estimate the carbon dioxide (CO[subscript 2]) flux in a domain centered on the city of Indianapolis, Indiana. One approach, Hestia, uses a

The INFLUX experiment has taken multiple approaches to estimate the carbon dioxide (CO[subscript 2]) flux in a domain centered on the city of Indianapolis, Indiana. One approach, Hestia, uses a bottom-up technique relying on a mixture of activity data, fuel statistics, direct flux measurement and modeling algorithms. A second uses a Bayesian atmospheric inverse approach constrained by atmospheric CO[subscript 2] measurements and the Hestia emissions estimate as a prior CO[subscript 2] flux. The difference in the central estimate of the two approaches comes to 0.94 MtC (an 18.7% difference) over the eight-month period between September 1, 2012 and April 30, 2013, a statistically significant difference at the 2-sigma level. Here we explore possible explanations for this apparent discrepancy in an attempt to reconcile the flux estimates. We focus on two broad categories: 1) biases in the largest of bottom-up flux contributions and 2) missing CO[subscript 2] sources. Though there is some evidence for small biases in the Hestia fossil fuel carbon dioxide (FFCO2) flux estimate as an explanation for the calculated difference, we find more support for missing CO[subscript 2] fluxes, with biological respiration the largest of these. Incorporation of these differences bring the Hestia bottom-up and the INFLUX inversion flux estimates into statistical agreement and are additionally consistent with wintertime measurements of atmospheric [superscript 14]CO[subscript 2]. We conclude that comparison of bottom-up and top-down approaches must consider all flux contributions and highlight the important contribution to urban carbon budgets of animal and biotic respiration. Incorporation of missing CO[subscript 2] fluxes reconciles the bottom-up and inverse-based approach in the INFLUX domain.

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Created

Date Created
  • 2017-08-03

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Optimizing the Spatial Resolution for Urban CO2 Flux Studies Using the Shannon Entropy

Description

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO[subscript 2]

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO[subscript 2] emissions are provided in the form of a group of sector-specific vector layers with point, line, and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO[subscript 2] sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume. The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO[subscript 2] emissions with varying resolutions in four Hestia study areas, and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO[subscript 2] emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO[subscript 2] flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO[subscript 2] emissions products is centered around 100 m, at which the FFCO[subscript 2] emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between FFCO[subscript 2] emissions and various social-economic variables at the U.S. census block group level.

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Created

Date Created
  • 2017-05-19

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Impacts of climate change on US commercial and residential building energy demand

Description

Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space

Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated.

Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations.

As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer.

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Created

Date Created
  • 2016